Data Science

• Introduction to Data Science
  • What is Data Science?
  • Data Science vs Machine Learning vs AI
  • The Data Science Lifecycle (Data Collection, Cleaning, Exploration, Modeling, Evaluation)
  • Key Skills in Data Science (Programming, Statistics, Data Visualization)
  • Applications of Data Science (Healthcare, Finance, Marketing, etc.)

DATA SCIENCE GET STARTED

• Setting Up the Data Science Environment
  • Installing Python and Anaconda
  • Jupyter Notebooks for Data Science
  • Python Libraries for Data Science (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)
  • IDE Setup (VS Code, PyCharm)
• Data Science Workflow
  • Understanding the Data Science Pipeline
  • Data Collection and Acquisition
  • Data Preparation and Cleaning
  • Data Exploration and Analysis

DATA SCIENCE FOUNDATIONS

• Data Types and Structures
  • Structured vs Unstructured Data
  • Data Formats (CSV, JSON, XML, SQL)
  • Working with DataFrames (Pandas)
  • Time Series Data
• Basic Statistics for Data Science
  • Descriptive Statistics (Mean, Median, Mode, Variance)
  • Probability Distributions (Normal Distribution, Binomial, Poisson)
  • Hypothesis Testing (T-tests, Chi-square, ANOVA)
  • P-values, Confidence Intervals, and Significance Levels
• Data Visualization Basics
  • Introduction to Data Visualization
  • Types of Visualizations (Bar Chart, Line Plot, Scatter Plot, Histogram)
  • Visualizing Relationships (Correlation Plots, Heatmaps)
  • Using Matplotlib and Seaborn for Visualization

DATA WRANGLING

• Data Cleaning and Preprocessing
  • Handling Missing Values (Imputation, Removal)
  • Removing Duplicates
  • Data Transformation (Normalization, Standardization)
  • Feature Engineering (Creating New Features, Binning, Encoding Categorical Data)
• Working with Different Data Types
  • Handling Categorical Data (Label Encoding, One-Hot Encoding)
  • Handling Text Data (Tokenization, Lemmatization, Stop Words)
  • Handling Date/Time Data (Datetime Manipulation, Time Series Data)

EXPLORATORY DATA ANALYSIS (EDA)

• EDA Overview
  • What is EDA? Importance in Data Science
  • Summary Statistics and Data Distribution
  • Visual Exploration of Data
• Correlation and Covariance
  • Understanding Relationships between Features
  • Pearson and Spearman Correlation
  • Covariance and its Use in Data Science
• Identifying Patterns and Outliers
  • Detecting Outliers (Z-scores, IQR)
  • Identifying Trends, Clusters, and Anomalies

MACHINE LEARNING IN DATA SCIENCE

• Supervised Learning in Data Science
  • Regression (Linear, Logistic)
  • Classification (Decision Trees, Random Forests, K-NN, SVM)
  • Model Evaluation Metrics (Confusion Matrix, ROC Curve, Cross-validation)
• Unsupervised Learning in Data Science
  • Clustering Algorithms (K-means, DBSCAN, Hierarchical Clustering)
  • Dimensionality Reduction (PCA, t-SNE, LDA)
  • Association Rule Mining (Apriori, FP-growth)
• Model Evaluation and Tuning
  • Hyperparameter Tuning (Grid Search, Random Search)
  • Cross-Validation Techniques
  • Regularization (Ridge, Lasso)

DATA SCIENCE IN PRACTICE

• Working with Big Data
  • Introduction to Big Data (Hadoop, Spark, Dask)
  • Data Storage and Management (SQL, NoSQL, Data Lakes)
  • Working with Distributed Systems (Hadoop, Apache Spark)
  • Handling Large Datasets (Optimizing Memory and Computation)
• Data Science Projects
  • End-to-End Data Science Projects (Problem Formulation, Data Collection, Model Deployment)
  • Kaggle Competitions
  • Real-world Examples (Predicting Stock Prices, Building Recommender Systems)

DATA SCIENCE TOOLS & LIBRARIES

• Pandas
  • DataFrames and Series
  • Data Cleaning and Transformation
  • Groupby and Aggregation
• NumPy
  • Arrays and Matrix Operations
  • Vectorization and Broadcasting
• Matplotlib & Seaborn
  • Creating Plots (Line, Scatter, Bar, Heatmaps)
  • Customizing Visualizations
• Scikit-learn
  • Supervised Learning (Classification, Regression)
  • Unsupervised Learning (Clustering, PCA)
  • Model Evaluation and Metrics
• TensorFlow and Keras (for Deep Learning)
  • Building Neural Networks for Data Science
  • Transfer Learning and Pretrained Models
  • Model Training, Tuning, and Deployment

DATA SCIENCE CHALLENGES & RESEARCH

• Ethics and Privacy in Data Science
  • Bias and Fairness in Data
  • Privacy Concerns (GDPR, Data Anonymization)
  • Explainability and Interpretability of Models
• Emerging Trends in Data Science
  • AutoML (Automated Machine Learning)
  • AI and ML Integration with Data Science
  • Edge Computing and Real-time Data Science
  • Federated Learning

CAREER IN DATA SCIENCE

• Skills Required for Data Science Careers
  • Technical Skills (Python, SQL, Machine Learning)
  • Soft Skills (Communication, Problem-Solving, Critical Thinking)
• Data Science Job Roles
  • Data Analyst, Data Scientist, Data Engineer, Machine Learning Engineer
• Building a Data Science Portfolio
  • Kaggle Projects
  • GitHub Repositories
  • Blogging and Sharing Insights